A Review and Analysis of the Impact of Homicide Measurement on Cross-national Research

Abstract

The number of cross-national homicide studies is increasing rapidly. Many scholars, however, do not consider the details of how the four different homicide data sources generate the national homicide rates used for this research/ We tested if research findings on trends, levels, and structural covariates of homicide are dependent on data source selection. We used 1990-2018 data in 5- and 10-year groupings, and also pooled over time and nation. We utilized exploratory data analysis techniques, specifically correlations and paired t-tests, to look for differences in homicide trends and rates. Then we employed a series of Seemingly Unrelated Regression (SUR) models to determine if associations with homicide of the typical structural covariates were dependent on homicide data source. Finally, we examined Wald Tests to determine if differences in the sizes of the SUR coefficients from each data source were significantly different from zero. We found differences in homicide trends and levels by data source. We also found that associations with homicide rates of structural covariates varied in significance, magnitude, and even direction depending on homicide data source. Cross-national homicide research has a promising future for understanding short- and long-term global and regional trends, population-level covariates of homicide, and constructing theoretical explanations for geographical and temporal variation. However, researchers must better understand how national homicide data are generated by nations and by these four data sources, and be cautious when selecting the source of their homicide rates. All four systems possess limitations, but homicide data from the World Health Organization’s Mortality Database present the most attractive option.

Citation:

Rogers, M.L. & Pridemore, W.A. (2023). A review and analysis of the impact of homicide measurement on cross-national research. Annual Review of Criminology.

Supplementary Material

Figure1 Yearly differences between World Bank World Development Indicators and United Nations Office on Drugs and Crime Homicide rates.

Figure 2 Modified Paris Plot for 10-Year Average Homicide Rates

Figure 2 provides a series of modified scatter plots for the 10-year average homicide rates across the four sources. Correlations between WHO rates and those from UNODC and WB WDI ranged from 0.79 (1990-1999, 2000-2009) to 0.85 (2010-2018). The WHO average homicide rate was significantly different from the UNODC and WB WDI rates for the 2000-2009 and 2010-2018 periods. WHO and GHO homicide rates were correlated at 0.77 (2001-2019) and 0.84 (2010-2018). The differences were statistically significant in both 10-year periods. Therefore, while the homicide rates from each source tend to trend together, the differences between their overall averaged rates are significant.

The UNODC and WB WDI rates were perfectly correlated across each of the 10-year periods. Like the 5-year averages, a few nations have 10-year average homicide rates that differ slightly between the two sources. The average homicide rates for UNODC nations were not significantly different from their counterparts in the WB WDI in any of the 10-year time frames.

The GHO and the UNODC and WB WDI homicide rates were nearly perfectly correlated, at 0.98 (2001-2009) and 0.99 (2010-2018). As with the 5-year averages, however, in each of the 10-year periods the average GHO rates were significantly different in magnitude from the UNODC and WB WDI rates.

Note: * p<0.001, r=Pearson's correlation coefficient, t=paired t-test with unequal variance t-statistics, p=p-value for paired t-test with unequal variance, WHO Mort=WHO Mortality, UNODC=United Nations Office on Drugs and Crime, World Bank=World Bank World Development Indicators, GHO=Global Health Observatory.

Figure 3 Predicted Homicide Rates within Sample by Data Source for 10-Year Periods

Figure 3 provides the best fit line representation of the models, along with the slope coefficient and p-value. These results are shown in tabular format in Table A3 of our Online Appendix. Figure 8 provides evidence that scholars would come to different conclusions for the effects of infant mortality, sex ratio, unemployment, and the education index on homicide rates depending upon the homicide data source they selected. During the 1990-1999 period, assuming the use of a two-tailed test, poverty was significantly associated with homicide rates when using WHO data but not with UNODC and WB WDI data (though it was significant when using the latter two data sources if employing a one-tailed test, which is not uncommon in this literature given theoretical expectations for many variables).

During the 2000-2009 period, sex ratio was not associated with homicide rates when using WHO and GHO data, but it was associated with homicide for UNODC and WB WDI data if a one-tailed test was used. For unemployment there were differences by data source in the 1990-1999 and 2000-2009 periods. In each period unemployment was not significantly associated with homicide rates when using WHO data, but it was significantly associated with homicide when using UNODC, WB WDI, and GHO data (though in the 2000-2009 range the associations were significant only when using a one-tailed test). There were multiple differences by data source for the education index. In all three 10-year periods, the education index was not significantly associated with homicide rates when using WHO data. In 1990-1999 and 2010-2018, however, it was significantly associated with homicide when using UNODC and WB WDI data (for the 1990-1999 period it was significant only with a one-tailed test). It was also significantly associated with homicide using GHO data in 2000-2009 (one-tailed test) and 2010-2018.

Note: *y-axis is unlike other plots in graph. WHO = WHO Mortality homicide data, UNODC=United Nations Office on Drugs and Crime homicide data. WB=World Bank World Development Indicators homicide data. GHO=WHO Global Health Observatory. GHO does not have data for 1990-1999. All other variables are held at their mean.

Figure 4 10-Year Average Wald Test Results

Figure 4 displays results when testing if the magnitude of the slope varies significantly by homicide data source. That is, is the difference in the slope coefficients for each variable significantly different from zero across all possible comparisons of data sources? In the 1990-1999 plot, sex ratio and unemployment both had differences in the slope coefficient when using WHO data relative to UNODC and WB WDI data. For sex ratio, however, once we applied a Bonferroni correction for multiple comparisons these differences were no longer significant.

Note: Red solid line is the F-critical statistics where p=0.05, the red dot dashed line is the F-critical statistics where p=0.10, the blue dotted line is the F-critical statistics where p=0.05 and a Bonferroni correction was applied, and the dashed blue line is the F-critical statistics where p=0.10 and a Bonferroni correction was applied. WHO= WHO Mortality homicide data, UNODC=United Nations Office on Drugs and Crime homicide data. WB=World Bank World Development Indicators homicide data, GHO=WHO Global Health Observatory homicide data.